The aberrant expression in epithelial cells of the mesenchymal isoform of FGFR2 controls the negative crosstalk between EMT and autophagy

Signalling of the epithelial splicing variant of fibroblast growth factor receptor 2 (FGFR2b) triggers both differentiation and autophagy, while the aberrant expression of the mesenchymal FGFR2c isoform in epithelial cells induces impaired differentiation, inhibition of autophagy as well as the induction of the epithelial-mesenchymal transition (EMT). In light of the widely proposed negative loop linking autophagy and EMT in the early steps of carcinogenesis, here we investigated the possible involvement of FGFR2c aberrant expression and signalling in orchestrating this crosstalk in human keratinocytes. Biochemical, molecular, quantitative immunofluorescence analysis and in vitro invasion assays, coupled to the use of specific substrate inhibitors and transient or stable silencing approaches, showed that AKT/MTOR and PKCε are the two hub signalling pathways, downstream FGFR2c, intersecting with each other in the control of both the inhibition of autophagy and the induction of EMT and invasive behaviour. These results indicate that the expression of FGFR2c, possibly resulting from FGFR2 isoform switch, could represent a key upstream event responsible for the establishment of a negative interplay between autophagy and EMT, which contributes to the assessment of a pathological oncogenic profile in epithelial cells

Evanescent-wave coupled right angled buried waveguide: Applications in carbon nanotube mode-locking

In this paper we present a simple but powerful subgraph sampling primitive that is applicable in a variety of computational models including dynamic graph streams (where the input graph is defined by a sequence of edge/hyperedge insertions and deletions) and distributed systems such as MapReduce. In the case of dynamic graph streams, we use this primitive to prove the following results: -- Matching: First, there exists an $\tilde{O}(k^2)$ space algorithm that returns an exact maximum matching on the assumption the cardinality is at most $k$. The best previous algorithm used $\tilde{O}(kn)$ space where $n$ is the number of vertices in the graph and we prove our result is optimal up to logarithmic factors. Our algorithm has $\tilde{O}(1)$ update time. Second, there exists an $\tilde{O}(n^2/\alpha^3)$ space algorithm that returns an $\alpha$-approximation for matchings of arbitrary size. (Assadi et al. (2015) showed that this was optimal and independently and concurrently established the same upper bound.) We generalize both results for weighted matching. Third, there exists an $\tilde{O}(n^{4/5})$ space algorithm that returns a constant approximation in graphs with bounded arboricity. -- Vertex Cover and Hitting Set: There exists an $\tilde{O}(k^d)$ space algorithm that solves the minimum hitting set problem where $d$ is the cardinality of the input sets and $k$ is an upper bound on the size of the minimum hitting set. We prove this is optimal up to logarithmic factors. Our algorithm has $\tilde{O}(1)$ update time. The case $d=2$ corresponds to minimum vertex cover. Finally, we consider a larger family of parameterized problems (including $b$-matching, disjoint paths, vertex coloring among others) for which our subgraph sampling primitive yields fast, small-space dynamic graph stream algorithms. We then show lower bounds for natural problems outside this family

A symmetry classification for a class of (2+1)-nonlinear wave equation

In this paper, a symmetry classification of a $(2+1)$-nonlinear wave equation $u_{tt}-f(u)(u_{xx}+u_{yy})=0$ where $f(u)$ is a smooth function on $u$, using Lie group method, is given. The basic infinitesimal method for calculating symmetry groups is presented, and used to determine the general symmetry group of this $(2+1)$-nonlinear wave equation

Effect of raloxifene on IGF-I and IGFBP-3 in postmenopausal women with breast cancer

The effect on the IGF system of 60 mg and 600 mg daily of raloxifene administered for 2 weeks prior to surgery was investigated in 37 postmenopausal women with breast cancer. Raloxifene significantly decreased insulin-like growth factor (IGF-I) as compared to placebo (P < 0.05) with no dose–response relationship. No significant change was observed in IGFBP-3, while the IGF-I/IGFBP-3 molar ratio was decreased by treatment, with a statistically significant effect only for the higher dose. Given that high plasma levels of IGF-I have been suggested as a risk factor for breast cancer, these findings provide further support for the potential activity of raloxifene in breast cancer prevention. © 2001 Cancer Research Campaign http://www.bjcancer.co